The present invention relates generally to the ability to code medical documents based upon weighted belief networks. In particular, the systems and methods disclosed herein relate to the ability to apply acceptable Medicare or other insurance codes to assist in reimbursement and billing of medical services provided. Unlike manual coding, which is currently employed, the present systems provide more accurate, consistent, and rapid identification of actionable codes within a medical record.
Despite rapid growth of innovation in other fields in recent decades, the world of medical information, including patient medical records, billing, referrals, and a host of other information, has enjoyed little to no useful consolidation, reliability, or ease-of-access, leaving medical professionals, hospitals, clinics, and even insurance companies with many issues, such as unreliability of medical information, uncertainty of diagnosis, lack of standard, and a slew of other related problems.
One of the challenges facing those in the medical or related areas is the number of sources of information, the great amount of information from each source, maintenance of data in a HIPAA compliant manner, and consolidation of such information in a manner that renders it meaningful and useful to those in the field in addition to patients. Obviously, this has contributed to increased medical costs and is perhaps largely attributed to the field suffering from an organized solution to better aid the medical professionals, to better aid those requiring more reliable patient history and those requiring more control and access over such information.
The concept of “big data” is already well established in the field of information technology. Big data is a collection of tools, techniques and methodologies used when data sets are large and complex that it becomes difficult or impossible to store, query, analyze or process using current database management and data warehousing tools or traditional data processing applications. The challenges of handling big data include capture, curation, storage, search, sharing, analysis and visualization. The trend to larger data sets is due to the proliferation of data capture devices and the ease of capturing and entering data from a wide variety of sources.
Due to the intrinsic issues prevalent with medical information—where very large amounts of clinical and administrative information are generated and stored as unstructured text and scanned documents, big data platforms and analysis is all but unheard of Additionally, even when the data is readily machine readable, often the ability to properly analyze complex medical terms and conditions is limited to specialized individuals who must manually review each medical document individually. Such methods of document review are slow, error prone, costly, and subject to different outcomes based upon the reviewer. In the context of analyzing a record for a Medicare reimbursable event (a process known as coding), this can result in a significant loss of revenue and possibly reduced treatment efficacy.
It is therefore apparent that an urgent need exists for tools that allow for the analysis of medical information in order to code medical records automatically and efficiently. Specifically, the utilization of weighted belief networks may enable rapid, accurate and automated coding of medical records.